Review of deep learning-based weed identification in crop fields
作者机构：School of Agricultural Engineering and Food ScienceShandong University of TechnologyZibo 255000ShandongChina Department of Agricultural EngineeringHarper Adams UniversityNewport TF108NBShropshireUK School of Computer Science and TechnologyShandong University of TechnologyZibo 255000ShandongChina Academy of Ecological Unmanned FarmZibo 255000ShandongChina
出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)
年 卷 期：2023年第16卷第4期
页 面：1-10页
核心收录：
基 金：supported by the Top Talents Program for One Case,One Discussion of Shandong Province(27 of the Shandong Provincial Government Office) Natural Science Foundation of Shandong Province(Grant No.ZR2021 QC154) the international cooperation project of the China Scholarship Council for cultivating innovative talents(Grant No.202201040005)
主 题：deep learning weed detection weed classification cation image segmentation Convolutional Neural Network image processing
摘 要：Automatic weed identification and detection are crucial for precision weeding *** recent years,deep learning(DL)has gained widespread attention for its potential in crop weed *** paper provides a review of the current research status and development trends of weed identification in crop fields based on *** an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification *** is placed on data sources and DL models applied to different technical ***,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed *** solutions are proposed to provide a reference for future research directions in weed identification.
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